Hiring: Staff Machine Learning Engineer, ML Platform Company: Braze Location: Austin, TX Job Posted Time: 2026-09-16 21:37:30 Employment Type: Hybrid Target Skills & Keywords : CI/CD, Feature Store, HIPAA, Infrastructure as Code, Kafka, Kubernetes, MLflow, Machine Learning, Model Registry, MongoDB, Python, RabbitMQ, Rails, Redis About the job Experience: •8+ years building and operating distributed systems in production, with depth in deployment and operations. You have designed services for scale and reliability, owned CI/CD and infrastructure as code, and run what you built under production load Required Skills: •Identify and drive the transformative initiatives that change how the team runs ML in production, whether that's replatforming our queueing and orchestration, overhauling deployment and cloud identity, or retiring a generation of infrastructure •Own the platform's technical vision and production quality bar. Set direction for how models are trained, deployed, served, and observed; lead incident response for ML systems; and drive the reliability and cost work that keeps the platform efficient at scale •Drive initiatives that span teams. Our platform builds on shared infrastructure, deployment tooling, and data systems owned with partner teams, and you carry the technical relationships with those teams •Raise the team's engineering quality through design review, code review, and production readiness for ML systems, and mentor other senior engineers and data scientists •Connect technical decisions to customer and business outcomes, and represent the team's technical perspective to product and engineering leadership Qualifications: •Applied hands-on capability in ML workloads in production. Training pipelines, model serving, feature systems, or ML platform tooling all count; deep modeling experience is a plus rather than a requirement •A technical leader who has owned direction for a team, led multi-quarter initiatives across team boundaries, and grown senior engineers, all while keeping a high personal output •Deep working knowledge of Kubernetes and cloud infrastructure, including identity and access management, networking, and the cost profile of what you run •An effective communicator, both verbal and written, whose designs and recommendations build consensus and drive forward decision making •Queueing and orchestration systems such as Celery, RabbitMQ, Kafka, or Ray •ML platform tooling such as MLflow or another model registry, feature stores, or ML observability •Operating under compliance regimes such as SOX or HIPAA •Customer engagement, personalization, or marketing technology domain experience Compensation: •Competitive benefits and rewards package •Competitive compensation that may include equity Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!